Pub Date : 2026-02-07DOI: 10.1016/j.sbi.2025.103214
Antonio J Ortiz, Antoniel A S Gomes, Pedro Renault, David Romero, Antoni Guillamon, Jesús Giraldo
Drug-target residence time (τ) is reviewed from two perspectives: mathematics and molecular dynamics. The first focuses on the quantification of τ using a mathematical formalism applicable to different pharmacological mechanistic conditions. This formalism is based on the concept of the smallest-modulus eigenvalue of a subsystem of interest, in which the global formation process has been eliminated. The second includes relevant studies of recent years to provide a structural explanation of τ predictions. Special attention is paid to physically supported artificial intelligence methods. The main objective of this minireview is to promote a combined approach in which mathematics and physics work synergistically to describe the complexity associated with τ in G protein-coupled receptors.
{"title":"Drug-target residence time: Analyzing cooperativity effects in G protein-coupled receptors by mathematical modeling and molecular dynamics simulations.","authors":"Antonio J Ortiz, Antoniel A S Gomes, Pedro Renault, David Romero, Antoni Guillamon, Jesús Giraldo","doi":"10.1016/j.sbi.2025.103214","DOIUrl":"https://doi.org/10.1016/j.sbi.2025.103214","url":null,"abstract":"<p><p>Drug-target residence time (τ) is reviewed from two perspectives: mathematics and molecular dynamics. The first focuses on the quantification of τ using a mathematical formalism applicable to different pharmacological mechanistic conditions. This formalism is based on the concept of the smallest-modulus eigenvalue of a subsystem of interest, in which the global formation process has been eliminated. The second includes relevant studies of recent years to provide a structural explanation of τ predictions. Special attention is paid to physically supported artificial intelligence methods. The main objective of this minireview is to promote a combined approach in which mathematics and physics work synergistically to describe the complexity associated with τ in G protein-coupled receptors.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"103214"},"PeriodicalIF":6.1,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146141361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1016/j.sbi.2025.103218
Ashar J Malik, Stephanie Portelli, David B Ascher
Transformers are rapidly reshaping structural biology. We argue the reason is "Emergent Latent Biology" (ELB): transformers place proteins into high-dimensional representations where hidden biophysical patterns become easier to see. We explore this concept across four key areas: protein folding, variant effects, protein-protein and protein-drug interactions. Highlighting recent gains, we note that traditional, physics-based calculations are still required for the hardest quantitative jobs, like predicting precise binding strength. Furthermore, we draw attention to major pitfalls, arguing progress depends on solving the critical "chemistry gap," modelling chemical modifications, and the "dynamics gap", predicting protein movement, which requires better validation methods and new large-scale experiments.
{"title":"Transformers as a substrate for structural biology.","authors":"Ashar J Malik, Stephanie Portelli, David B Ascher","doi":"10.1016/j.sbi.2025.103218","DOIUrl":"https://doi.org/10.1016/j.sbi.2025.103218","url":null,"abstract":"<p><p>Transformers are rapidly reshaping structural biology. We argue the reason is \"Emergent Latent Biology\" (ELB): transformers place proteins into high-dimensional representations where hidden biophysical patterns become easier to see. We explore this concept across four key areas: protein folding, variant effects, protein-protein and protein-drug interactions. Highlighting recent gains, we note that traditional, physics-based calculations are still required for the hardest quantitative jobs, like predicting precise binding strength. Furthermore, we draw attention to major pitfalls, arguing progress depends on solving the critical \"chemistry gap,\" modelling chemical modifications, and the \"dynamics gap\", predicting protein movement, which requires better validation methods and new large-scale experiments.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"103218"},"PeriodicalIF":6.1,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1016/j.sbi.2025.103216
Utkarsh Upadhyay, Anton Dorn, Christian Faber, Alexander Schug
RNA structure prediction remains one of the most challenging problems in computational biology, with significant implications for understanding gene regulation, drug design, and synthetic biology. While deep learning has revolutionized protein structure prediction, RNA presents unique challenges including limited training data, complex noncanonical interactions, and conformational flexibility. This review examines the evolution from traditional physics-based methods to current deep learning approaches for RNA secondary and tertiary structure prediction. After briefly exploring traditional methods, like Direct Coupling Analysis and physics-based simulations, we systematically review three deep learning paradigms: language model-based methods, end-to-end structure predictors, and geometry-distance prediction approaches. Furthermore, we identify critical future research directions focusing on advanced tokenization strategies to address data scarcity and explainable artificial intelligence techniques to improve model interpretability. Despite significant progress, achieving transformative performance requires continued methodological innovation, specifically designed for RNA's unique characteristics, and a substantial expansion of high-quality structural datasets.
{"title":"From sequence to structure: A comprehensive review of deep learning models for RNA structure prediction.","authors":"Utkarsh Upadhyay, Anton Dorn, Christian Faber, Alexander Schug","doi":"10.1016/j.sbi.2025.103216","DOIUrl":"https://doi.org/10.1016/j.sbi.2025.103216","url":null,"abstract":"<p><p>RNA structure prediction remains one of the most challenging problems in computational biology, with significant implications for understanding gene regulation, drug design, and synthetic biology. While deep learning has revolutionized protein structure prediction, RNA presents unique challenges including limited training data, complex noncanonical interactions, and conformational flexibility. This review examines the evolution from traditional physics-based methods to current deep learning approaches for RNA secondary and tertiary structure prediction. After briefly exploring traditional methods, like Direct Coupling Analysis and physics-based simulations, we systematically review three deep learning paradigms: language model-based methods, end-to-end structure predictors, and geometry-distance prediction approaches. Furthermore, we identify critical future research directions focusing on advanced tokenization strategies to address data scarcity and explainable artificial intelligence techniques to improve model interpretability. Despite significant progress, achieving transformative performance requires continued methodological innovation, specifically designed for RNA's unique characteristics, and a substantial expansion of high-quality structural datasets.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"103216"},"PeriodicalIF":6.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.sbi.2025.103215
Si Zhang, Gregory R. Bowman
Cryptic pockets are promising targets for drug discovery that greatly expand the druggable proteome. In particular, they can provide opportunities to target proteins previously thought to be “undruggable” due to a lack of pockets in structures of the ground state. However, their transient and hidden nature renders them difficult to detect through conventional experimental screening methods. Recent advances in computational methodologies and resources have greatly enhanced our ability to identify and characterize such elusive pockets. This review highlights key developments in computational approaches, including physics-based molecular dynamics simulations, artificial intelligence–driven models, and hybrid strategies that integrate both to enhance cryptic pocket discovery and functional interpretation.
{"title":"Decrypting cryptic pockets with physics-based simulations and artificial intelligence","authors":"Si Zhang, Gregory R. Bowman","doi":"10.1016/j.sbi.2025.103215","DOIUrl":"10.1016/j.sbi.2025.103215","url":null,"abstract":"<div><div>Cryptic pockets are promising targets for drug discovery that greatly expand the druggable proteome. In particular, they can provide opportunities to target proteins previously thought to be “undruggable” due to a lack of pockets in structures of the ground state. However, their transient and hidden nature renders them difficult to detect through conventional experimental screening methods. Recent advances in computational methodologies and resources have greatly enhanced our ability to identify and characterize such elusive pockets. This review highlights key developments in computational approaches, including physics-based molecular dynamics simulations, artificial intelligence–driven models, and hybrid strategies that integrate both to enhance cryptic pocket discovery and functional interpretation.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103215"},"PeriodicalIF":6.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.sbi.2025.103217
Riccardo Solazzo , Shu-Yu Chen , Sereina Riniker
Proteolysis-targeting chimeras (PROTACs) and molecular glues promote targeted protein degradation by recruiting an E3 ligase to proteins of interest (POIs). An accurate 3D structure of the ternary complex formed by E3 ligase, ligand, and POI is central to the rational design of degraders. Elucidating this structure with crystallography or cryo-EM can be challenging due to conformational flexibility, dynamic protein-protein interactions, and high-dimensional binding landscapes. To facilitate structure-based design in the absence of an experimental structure, computational approaches have been proposed: (i) multistep methods involving traditional docking pipelines, and (ii) single-step methods with deep learning models to directly predict the complex structure. Multistep methods are limited by sampling complexity, accurate input structures, scoring accuracy, and computational cost, while single-step methods are faster but are constrained by training-data scarcity. Here, we examine recent advances and emerging tools in modeling ternary complexes, critically discuss their predictive power and limitations, and highlight remaining challenges.
{"title":"Machine learning, docking, or physics for structure prediction of ligand-induced ternary complexes","authors":"Riccardo Solazzo , Shu-Yu Chen , Sereina Riniker","doi":"10.1016/j.sbi.2025.103217","DOIUrl":"10.1016/j.sbi.2025.103217","url":null,"abstract":"<div><div>Proteolysis-targeting chimeras (PROTACs) and molecular glues promote targeted protein degradation by recruiting an E3 ligase to proteins of interest (POIs). An accurate 3D structure of the ternary complex formed by E3 ligase, ligand, and POI is central to the rational design of degraders. Elucidating this structure with crystallography or cryo-EM can be challenging due to conformational flexibility, dynamic protein-protein interactions, and high-dimensional binding landscapes. To facilitate structure-based design in the absence of an experimental structure, computational approaches have been proposed: (i) multistep methods involving traditional docking pipelines, and (ii) single-step methods with deep learning models to directly predict the complex structure. Multistep methods are limited by sampling complexity, accurate input structures, scoring accuracy, and computational cost, while single-step methods are faster but are constrained by training-data scarcity. Here, we examine recent advances and emerging tools in modeling ternary complexes, critically discuss their predictive power and limitations, and highlight remaining challenges.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103217"},"PeriodicalIF":6.1,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.sbi.2025.103210
Giulio Tesei , Francesco Pesce , Kresten Lindorff-Larsen
Protein design has the potential to revolutionize biotechnology and medicine. While most efforts have focused on proteins with well-defined structures, increased recognition of the functional significance of intrinsically disordered regions, together with improvements in their modeling, has paved the way to their computational design. This review summarizes recent advances in designing intrinsically disordered regions with tailored conformational ensembles, molecular recognition, and phase behavior. We discuss challenges in combining models of predictive accuracy with scalable design workflows and outline emerging strategies that integrate knowledge-based, physics-based, and machine-learning approaches.
{"title":"Computational design of intrinsically disordered proteins","authors":"Giulio Tesei , Francesco Pesce , Kresten Lindorff-Larsen","doi":"10.1016/j.sbi.2025.103210","DOIUrl":"10.1016/j.sbi.2025.103210","url":null,"abstract":"<div><div>Protein design has the potential to revolutionize biotechnology and medicine. While most efforts have focused on proteins with well-defined structures, increased recognition of the functional significance of intrinsically disordered regions, together with improvements in their modeling, has paved the way to their computational design. This review summarizes recent advances in designing intrinsically disordered regions with tailored conformational ensembles, molecular recognition, and phase behavior. We discuss challenges in combining models of predictive accuracy with scalable design workflows and outline emerging strategies that integrate knowledge-based, physics-based, and machine-learning approaches.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103210"},"PeriodicalIF":6.1,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1016/j.sbi.2025.103213
Simon Olsson
Understanding biomolecular function depends on bridging experimental observables with models that capture structural, stationary, and dynamical properties. Molecular dynamics (MD) simulations, in principle provide a bridge, but the sampling problem remains a fundamental roadblock toward this goal. In this mini-review, I outline recent progress in the area of Generative MD (GenMD)—an approach where generative AI (GenAI) is used to mimic the statistical distributions resulting from MD simulations, which are inaccessible using current numerical algorithms. Here, I highlight a few exemplars of GenMD and then outline open problems and current limitations.
{"title":"Generative molecular dynamics","authors":"Simon Olsson","doi":"10.1016/j.sbi.2025.103213","DOIUrl":"10.1016/j.sbi.2025.103213","url":null,"abstract":"<div><div>Understanding biomolecular function depends on bridging experimental observables with models that capture structural, stationary, and dynamical properties. Molecular dynamics (MD) simulations, in principle provide a bridge, but <em>the sampling problem</em> remains a fundamental roadblock toward this goal. In this mini-review, I outline recent progress in the area of Generative MD (GenMD)—an approach where generative AI (GenAI) is used to mimic the statistical distributions resulting from MD simulations, which are inaccessible using current numerical algorithms. Here, I highlight a few exemplars of GenMD and then outline open problems and current limitations.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103213"},"PeriodicalIF":6.1,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.sbi.2025.103199
Matthias Buck, Monika Fuxreiter
{"title":"Editorial overview: Exploring protein conformational landscapes for catalysis in the beginning of the artificial intelligence era","authors":"Matthias Buck, Monika Fuxreiter","doi":"10.1016/j.sbi.2025.103199","DOIUrl":"10.1016/j.sbi.2025.103199","url":null,"abstract":"","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103199"},"PeriodicalIF":6.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145818466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1016/j.sbi.2025.103196
Tao Li, Sheng-You Huang
Cryo-electron microscopy (cryo-EM) has emerged as one of the most powerful techniques for determining the structures of biological macromolecules. The ultimate goal of cryo-EM is to determine the atomic structure of target molecules, where map postprocessing and atomic-model building are two crucial final steps of the cryo-EM pipeline. With the fast development of artificial intelligence, deep learning has been implemented in various stages of cryo-EM. Here, we present a comprehensive overview of recent advances in map postprocessing and model building for cryo-EM maps with focuses on deep learning–based methods. We also discuss the advantages and limitations of current approaches as well as challenges that are left for future research.
{"title":"Deep learning–based postprocessing and model building for cryo-electron microscopy maps","authors":"Tao Li, Sheng-You Huang","doi":"10.1016/j.sbi.2025.103196","DOIUrl":"10.1016/j.sbi.2025.103196","url":null,"abstract":"<div><div>Cryo-electron microscopy (cryo-EM) has emerged as one of the most powerful techniques for determining the structures of biological macromolecules. The ultimate goal of cryo-EM is to determine the atomic structure of target molecules, where map postprocessing and atomic-model building are two crucial final steps of the cryo-EM pipeline. With the fast development of artificial intelligence, deep learning has been implemented in various stages of cryo-EM. Here, we present a comprehensive overview of recent advances in map postprocessing and model building for cryo-EM maps with focuses on deep learning–based methods. We also discuss the advantages and limitations of current approaches as well as challenges that are left for future research.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103196"},"PeriodicalIF":6.1,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145767354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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